Data governance · Data quality
Data you can trust. Decisions you can defend.
We help regulated organisations build governance that works in practice — clear ownership, measurable quality and lineage your regulator can follow from source to report.
Why clients choose us
A practitioner-led consultancy built around evidence, not slideware.
Most programmes stall because the disciplines are run in isolation. We design them to reinforce one another: policy drives quality rules, the catalogue makes ownership visible, and lineage proves it all works.
Practitioner-led
Every engagement is led by consultants who have run governance programmes inside banks, insurers and public bodies.
Framework-grounded
Our methods align with DAMA-DMBOK, ISO 8000 and regulatory principles, so your approach stands up to scrutiny.
Measurable outcomes
We agree the metrics up front — quality scores, lineage coverage, steward activity — and report against them.
Our expertise
Seven disciplines, delivered as one programme
Seven disciplines, one goal: data your organisation can rely on.
Data Governance
Operating models, roles and councils that turn data ownership from an org chart into a working discipline.
Learn more → DQData Quality
Frameworks, rules and scorecards that make data quality measurable, owned and continuously improving.
Learn more → CATData Catalogue & Metadata
Business glossaries, metadata models and lineage that make data findable, understood and trusted.
Learn more → POLPolicy Management
Policy, standard and procedure hierarchies mapped to regulation, classification, privacy and retention.
Learn more → TLSTools & Platforms
Vendor-neutral platform selection, implementation, integration and health checks for governance tooling.
Learn more → ENGStakeholder Engagement
Mapping, briefings, onboarding and champion networks that turn governance from a mandate into a movement.
Learn more → ADVConsulting Services
Assessments, roadmaps, programme management and interim data leadership — from first diagnosis to retained advice.
Learn more → →Start with a conversation, not a proposal
Book a consultation →Outcomes
Evidence from recent engagements
How we work
A four-stage approach that ends in ownership, not dependency
- 01
Assess
Maturity baseline against DAMA-DMBOK, critical data elements identified, pain points quantified.
- 02
Design
Operating model, policies, quality rules and target-state architecture, agreed with the people who will run them.
- 03
Deliver
Catalogue, lineage and quality controls implemented in sprints, with stewards onboarded as we go.
- 04
Sustain
KPIs, councils and champion networks handed over so the capability keeps improving after we leave.
Platforms we work with
We are vendor-neutral. We recommend what fits your estate, not what pays a referral fee.
- Collibra
- Informatica EDC & Axon
- Databricks Unity Catalog
- Microsoft Purview
- Alation
- Ataccama
Latest insights
Thinking from the practice
Why lineage is the fastest route to BCBS 239 evidence
Start with a material risk report and work backwards. Evidence-led lineage connects reported numbers to the controls, decisions and accountable owners behind them.
Building a data quality scorecard your board will read
A board scorecard should explain exposure and the decisions required, not display every validation rule. Build it around critical outcomes, transparent measures and accountable action.
Turning nominated stewards into active ones
A name in a stewardship register does not create operational ownership. Give stewards bounded decisions, protected capacity and a route for resolving disagreements.
Start with a conversation, not a proposal
Tell us where your data is letting you down. We will come back within two working days with a clear view of what it takes to fix it.